Senior Snowflake Data Modeler - Investment Analytics

AllianceBernstein•Nashville, TN

1d•Onsite

About The Position

The Investment Technology team is seeking a Nashville, TN based Snowflake Data Modeler to design and govern the analytical data foundations supporting our investment processes. You will focus on building high‑quality Snowflake data models and semantic layers that power reporting, analytics, and AI‑enabled insights across portfolio management, risk, and research. This role is ideal for someone who excels at data modeling, understands analytics consumption, and enjoys working closely with investment professionals in a highly analytical environment. This role partners closely with data engineers, architects, and investment stakeholders but is primarily focused on modeling and consumption, not pipeline engineering.

Requirements

Strong expertise in Snowflake data modeling for analytics and reporting use cases.
Advanced SQL skills for modeling, transformation, and performance optimization in Snowflake.
Deep experience with dimensional modeling, star/snowflake schemas, and domain‑driven design.
Experience designing data models to support Power BI, including semantic alignment and metric consistency.
Familiarity with BI consumption patterns and performance considerations.
Experience implementing data validation, reconciliation, and quality frameworks.
Working knowledge of metadata, lineage, and governance concepts (e.g., Purview or similar tools).
Understanding of how well‑structured models support AI, search, and natural‑language analytics.
Familiarity with semantic layers, business glossaries, and model discoverability.
Experience or strong interest in investment data domains (fixed income, equities, multi‑asset preferred).
Strong analytical thinking and attention to detail.
Ability to communicate modeling concepts clearly to both technical and investment audiences.
Collaborative mindset and comfort working across engineering, analytics, and business teams.

Responsibilities

Serve as a Snowflake‑focused data modeler, responsible for designing, evolving, and governing data models that power investment analytics, reporting, and downstream applications.
Emphasize dimensional and domain modeling, semantic layer design, and data quality, enabling research analysts, portfolio managers, risk teams, and quantitative users to access clean, well‑structured data.
Design and maintain dimensional, domain‑oriented, and analytics‑optimized data models in Snowflake.
Develop fact and dimension models supporting portfolio management, risk, attribution, research, and performance analytics.
Optimize Snowflake schemas for performance, scalability, and ease of consumption.
Build and maintain semantic layers and curated views that abstract underlying complexity for BI and analytics tools.
Ensure Snowflake models align with Power BI datasets, metrics, hierarchies, and business definitions.
Partner with analytics teams to enable consistent metric definitions and reusable analytical patterns.
Define and implement data quality rules, validation checks, and reconciliation logic within modeled datasets.
Collaborate with data governance teams to document data definitions, lineage, and usage standards.
Support controlled access patterns, role‑based views, and governed data exposure.
Build strong understanding of key investment data domains including reference data, pricing, terms & conditions, indices, risk inputs, and derived analytics.
Model data in ways that reflect how investment teams consume and analyze information.
Ensure that data relationships and grain are clearly defined and analytically sound.
Design Snowflake models that are AI‑ready, with consistent schemas, clear relationships, and rich metadata.
Support semantic consistency to enable natural‑language querying and AI‑driven analytics.
Partner with architecture teams on Snowflake‑based semantic and knowledge layers that support GenAI use cases.
Work closely with data engineers to ensure pipelines land data in structures optimized for modeling.
Partner with investment teams to translate analytical requirements into robust data models.
Contribute to modeling standards, best practices, and reusable design patterns across the organization.